High-entropy alloys (HEAs) are emerging as transformative binder materials for tungsten carbide (WC) hardmetals, offering a compelling alternative to conventional cobalt binders, which face mounting supply-chain risks and regulatory scrutiny. This computational project uses state-of-the-art machine learning interatomic potentials, trained on first-principles data, to predict the elastic properties of candidate HEA binders with near-DFT accuracy at a fraction of the computational cost. This enables high-throughput exploration of compositional spaces inaccessible to experimental trial-and-error.
By mapping stiffness, bulk and shear moduli, and elastic anisotropy across diverse HEA systems, and by capturing the chemical complexity and local lattice distortions intrinsic to these alloys, the project will identify binder compositions that deliver strong WC–binder cohesion, mechanical compatibility, and robust performance at elevated temperatures. The outcomes directly target industrial needs in cutting tools, mining and drilling equipment, wear-resistant components, and metal-forming dies, accelerating the development of cobalt-free hardmetals and supporting the industry’s transition toward more sustainable, high-performance, and supply-resilient solutions.
You will have a unique opportunity to work closely with the multidisciplinary, international team at Sandvik. You will develop transferable skills in supercomputing, data science, and molecular modelling; gain practical knowledge of mining and metallurgical processes; and gain experience applying modelling tools to real industrial problems. The collaboration between Curtin University and Sandvik will support you in growing into an effective and independent researcher, building long-term career capability in computational materials and chemical research.
Aim
This project aims to provide an atomistic-level understanding of the structural and mechanical properties of hardmetals, specifically cemented carbide materials that use a high-entropy alloy as the binder phase, in place of conventional cobalt. It seeks to establish the fundamental relationships between binder composition, atomic structure, and bulk mechanical response, so that this understanding can guide the rational design of cobalt-free hardmetals. A central aim is to demonstrate that machine learning interatomic potentials, trained on first-principles data, can describe the chemical complexity of HEA binders and the WC–binder interface with the accuracy needed for predictive materials design, while remaining computationally efficient enough for high-throughput screening.
Objectives
The principal objective of this project is to develop a computational framework capable of modelling, with high accuracy, the complex structural and elastic properties of WC–HEA composite materials. This may include:
- Building a reliable, first-principles-based modelling capability for HEA binders, the WC phase, and the WC–binder interface, capable of capturing the chemical complexity and local distortions intrinsic to high-entropy systems.
- Characterising the structure, phase stability, and elastic properties of candidate HEA binders, and identifying compositions that offer strong WC–binder cohesion and mechanical compatibility.
- Investigating the deformation mechanisms of the WC–HEA system, including its mechanical response under load and at elevated temperatures.
- Applying the framework to interpret experimental data and to provide predictive guidance that supports binder selection and process optimisation in collaboration with Sandvik.
- Develop a multi-scale model in collaboration with colleagues in the Mechanical Engineering department
Significance
This project will approach the design of cobalt-free hardmetals from a new perspective, applying innovative methodological approaches relative to those traditionally used in this field. A combination of ab initio, machine learning, and classical computational methods will be adopted to deliver an atomistic-scale picture of the mechanical and structural properties of hardmetals in which high-entropy alloys serve as the binder phase. The new computational framework developed here will allow for a realistic description of these chemically complex systems, where conventional modelling approaches struggle to capture local distortions and configurational disorder.
The project offers a unique opportunity to demonstrate how fundamental research can generate significant, practical outputs for an end-user problem. The modelling results will be used to interpret experimental data and to provide predictive guidance that informs process optimisation and the design of new materials and processes.
The project is closely aligned with Curtin University’s strategic Planet and Partnership priorities, and with Sandvik’s priority of understanding and improving its production processes and the in-service response of its materials. By accelerating the discovery of viable cobalt-free binders, the research contributes directly to more sustainable and supply-resilient manufacturing.
Ideal Candidate
Additionally, the applicants should meet the eligibility criteria for entry into a PhD program at Curtin University.
This project is open to International and Domestic applicants.
Internship (If there is one)
Through this project you will also have an internship opportunity. More information will be provided at a later date.
Scholarship
If you are identified as the preferred candidate for this project, you may be considered for an RTP scholarship.
Enquires and How to Apply
For enquires about this opportunity contact Professor Paolo Raiteri at P.Raiteri@curtin.edu.au
To formally apply submit an Expression of Interest to Professor Paolo Raiteri during the Central Scholarship round (July 1st – July 31st 2026)